Senior MLOps & Data Systems Engineer
New
L
LimeMicromobility
CanadaFull-TimeSenior
Salary141,000 - 194,000 CAD per year
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Job Details
- Experience
- 5+ years
- Required Skills
- AWSDockerPythonJenkinsKubeflowMLFlowPyTorchAirflowTensorflowCI/CDGitHub ActionsMLOpsComputer Vision
Requirements
- 5+ years of industry experience in MLOps, ML infrastructure, data systems, Machine Learning Engineering, or related roles.
- Strong programming skills in Python, with experience in ML frameworks such as PyTorch or TensorFlow.
- Experience building and maintaining end-to-end ML pipelines, including data ingestion, annotation, training, evaluation, and deployment workflows.
- Experience designing or integrating annotation and data curation workflows, and understanding how labeled data impacts model performance.
- Strong understanding of dataset versioning, data lineage, and reproducibility in machine learning systems.
- Experience with experiment tracking and model lifecycle management.
- Familiarity with CI/CD tools (e.g., GitHub Actions, GitLab CI, Jenkins) and applying them to machine learning workflows.
- Experience with containerization (Docker) and workflow orchestration systems.
- Experience with cloud-based ML environments (e.g., AWS) and distributed training workflows.
- Strong understanding of real-world data challenges, including noisy inputs, edge cases, and variability across environments.
- Strong problem-solving and debugging skills, particularly in complex, multi-stage systems.
- Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, or a related field (or equivalent practical experience).
Responsibilities
- Design, build, and maintain scalable pipelines that span data ingestion, annotation, validation, training, evaluation, and deployment, ensuring reproducibility, consistency, and traceability across the full ML lifecycle.
- Build and integrate annotation workflows with upstream data ingestion and training systems, enabling efficient task creation, labeling, QA, and dataset updates that directly support model iteration.
- Analyze model performance and failures, and drive targeted data improvements by connecting production signals, data mining, and annotation workflows into continuous feedback loops.
- Implement systems for experiment tracking, dataset versioning, and model lineage to enable reliable comparison and iteration across experiments.
- Develop and maintain CI/CD workflows tailored to ML systems, enabling automated testing, validation, and deployment of models and pipelines.
- Collaborate with embedded and platform teams to support the deployment of models to edge environments, ensuring compatibility, performance, and reliability.
- Implement monitoring, logging, and feedback systems to track model performance in production and drive continuous improvement through data and model iteration.
- Optimize training and inference workflows across cloud environments, including efficient utilization of GPU and compute resources.
- Work closely with applied scientists, embedded engineers, and data teams to ensure alignment across data workflows, model development, and deployment systems.
- Participate in and improve the full ML lifecycle, from raw data ingestion and annotation through training, evaluation, deployment support, and post-deployment analysis.
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